Uniform Tensor Clustering by Jointly Exploring Sample Affinities of Various Orders

Hongmin Cai*, Fei Qi, Junyu Li, Yu Hu, Bin Hu, Yue Zhang, Yiu-Ming Cheung*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

Traditional clustering methods rely on pairwise affinity to divide samples into different subgroups. However, high-dimensional small-sample (HDLSS) data are affected by the concentration effects, rendering traditional pairwise metrics unable to accurately describe relationships between samples, leading to suboptimal clustering results. This article advances the proposition of employing high-order affinities to characterize multiple sample relationships as a strategic means to circumnavigate the concentration effects. We establish a nexus between different order affinities by constructing specialized decomposable high-order affinities, thereby formulating a uniform mathematical framework. Building upon this insight, a novel clustering method named uniform tensor clustering (UTC) is proposed, which learns a consensus low-dimensional embedding for clustering by the synergistic exploitation of multiple-order affinities. Extensive experiments on synthetic and real-world datasets demonstrate two findings: 1) high-order affinities are better suited for characterizing sample relationships in complex data and 2) reasonable use of different order affinities can enhance clustering effectiveness, especially in handling high-dimensional data.
Original languageEnglish
Article number10636091
Pages (from-to)1-15
Number of pages15
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
Publication statusE-pub ahead of print - 14 Aug 2024

User-Defined Keywords

  • Tensors
  • Matrix decomposition
  • Computer science
  • Kernel
  • Clustering methods
  • Spectral analysis
  • Sequential analysis
  • Clustering
  • fusing affinity
  • high-order affinity
  • spectral graph
  • tensor

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